Performance assessment data is an important aspect of the business, analysis, and appreciation of professional/fantasy sports, stock markets, mutual funds, personal fitness, student education, video gaming, consumer sales, and so on. Athletic teams, coaches, scouts, agents, and fans evaluate performance data and statistics for comparing the performance of teams and individual athletes. Game strategy and player potential are often based on predictive models using this data. Similarly, organizations and individuals evaluate corporate performance data to rank performance, reward good performance, and provide development assistance. Student test scores are also used to evaluate educational strategy; fitness and health statistics are monitored for efficient personal training; and financial charts are analyzed to alleviate stock risks. The advantages of data processing and analysis are well understood and appreciated. However, data processing and analysis is not always user friendly as understanding a large amount of structured data is a daunting task.
One approach for making sense of raw performance data relies on human expertise. For example, in the area of athletics, domain experts (e.g., coaches, scouts, managers, analysts, statisticians etc.) are typically relied on to effectively convert raw data into human readable/useful knowledge. Batting averages, field goal percentages, successive streaks are considered, inter alia, to determine success against certain players or potential against future opponents. Human domain experts can “humanize” this raw data and convert numbers and statistics into insightful prose/narrative. But, effectively analyzing performance data requires consideration of incredible amounts of information to reduce variable uncertainty. Bulk number crunching becomes a difficult task when the valuable insight is drowned in a sea of numbers and statistics. Therefore, this manual based approach to identifying performance metrics consumes both time and resources.
In another example, the popularity of fantasy sports has converted millions of fans into expert statisticians for scrutinizing a professional athlete's performance data. Fantasy leagues allow the virtual assembly of teams comprising actual athletes to compete with other virtual teams based upon those players' real-life performance. The sports and players represented through fantasy games are widespread. The number of applications providing fantasy leagues, often over the Internet, is similarly extensive. However, each may provide a unique way of scoring and rewarding player performance. Accordingly, the value of each player's performance data may vary across different leagues and sports.
Advancements in technology and computerized data processing have made a wealth of performance statistics readily available for coaches and fantasy owners alike to review. Individual player statistics may give insight to an athlete's speed, movement, skills, and agility against one or more opponents. However, processing this data and placing value on relevant statistics varies between managers, leagues, and sports. Manually digesting performance data can be cumbersome in light of the current number of statistical categories monitored. As the type and number of data collected increased, more practical methods were developed for useful volumetric data processing.
In one approach for volumetric processing of raw performance statistics, predictive modeling systems are used. Using a more automated approach to analyze a large quantity of data, an example modeling system associated with fantasy sports leagues is disclosed in U.S. patent application Ser. No. 12/111,054, U.S. Publication No. 2008/0281444 A1, filed Apr. 28, 2008, to Krieger et al. for a “Predictive Modeling System and Method for Fantasy Sports,” which is hereby incorporated by reference in its entirety. This system contemplates a predictive modeling engine for generating relationships among player data and provides projections based on the relationships.
However, current systems for predictive analytics typically generalize known patterns to new data for projecting player performances. Additionally, these predictive modeling systems rarely consider the unique priority various users place on certain data sets. The predictive results are typically as hard to digest and read as the raw data itself to the average human user.
In contrast to generalizing known patterns to new data, data mining emphasizes discovering previously unknown patterns in new data sets. Data mining has recently experienced growth in the area of performance assessment. Performance assessment benefits from discovering unknown strengths and weaknesses as opposed to assessing patterns of current performance. The advantages of domain experts (e.g., coaches, teachers, interactive gamers, and the like) in analyzing performance metrics are based on the inherent expertise of these individuals to detect unknown patterns through subjective approaches. Therefore, an effective method of automatically analyzing performance assessment data enhances alternative statistical evaluation with data mining to discover patterns that are systematically difficult to detect, especially when dealing with dynamic data sets.
Additionally, current systems modeling performance assessment may not provide results in a user-friendly manner, as discussed above. Supplemental tables and graphs are often created to reflect the results of predictive modeling and still require additional processing and analysis. Subjective priority is neither accounted for nor presented and additional steps of manual data processing required. Accordingly, an improved system and method for automated processing, categorizing, and presenting performance assessment data is desirable.